Overview

Dataset statistics

Number of variables22
Number of observations3575
Missing cells47
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory614.6 KiB
Average record size in memory176.0 B

Variable types

Numeric13
Categorical9

Alerts

Title has a high cardinality: 273 distinct values High cardinality
Issn has a high cardinality: 273 distinct values High cardinality
Publisher has a high cardinality: 134 distinct values High cardinality
Coverage has a high cardinality: 104 distinct values High cardinality
Categories has a high cardinality: 1033 distinct values High cardinality
df_index is highly correlated with Rank and 1 other fieldsHigh correlation
Rank is highly correlated with df_index and 3 other fieldsHigh correlation
Sourceid is highly correlated with CountryHigh correlation
SJR is highly correlated with SJR Quartile and 3 other fieldsHigh correlation
H index is highly correlated with SJR Quartile and 1 other fieldsHigh correlation
Total Docs. (3years) is highly correlated with Total Refs. and 3 other fieldsHigh correlation
Total Refs. is highly correlated with Total Docs. (3years) and 3 other fieldsHigh correlation
Total Cites (3years) is highly correlated with SJR and 4 other fieldsHigh correlation
Citable Docs. (3years) is highly correlated with Total Docs. (3years) and 3 other fieldsHigh correlation
Cites / Doc. (2years) is highly correlated with SJRHigh correlation
Ref. / Doc. is highly correlated with SJRHigh correlation
Year is highly correlated with df_index and 1 other fieldsHigh correlation
Total Docs. per Year is highly correlated with Total Docs. (3years) and 3 other fieldsHigh correlation
Region is highly correlated with CountryHigh correlation
Country is highly correlated with Rank and 5 other fieldsHigh correlation
Type is highly correlated with CountryHigh correlation
SJR Quartile is highly correlated with Rank and 3 other fieldsHigh correlation
Publisher has 47 (1.3%) missing values Missing
df_index has unique values Unique
SJR has 336 (9.4%) zeros Zeros
Total Docs. (3years) has 186 (5.2%) zeros Zeros
Total Refs. has 105 (2.9%) zeros Zeros
Total Cites (3years) has 202 (5.7%) zeros Zeros
Citable Docs. (3years) has 190 (5.3%) zeros Zeros
Cites / Doc. (2years) has 227 (6.3%) zeros Zeros
Ref. / Doc. has 105 (2.9%) zeros Zeros
Total Docs. per Year has 84 (2.3%) zeros Zeros

Reproduction

Analysis started2022-10-06 22:34:17.462579
Analysis finished2022-10-06 22:35:07.233819
Duration49.77 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct3575
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5655.44
Minimum0
Maximum13283
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:07.410818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile207.4
Q11477.5
median4978
Q39435.5
95-th percentile13099.3
Maximum13283
Range13283
Interquartile range (IQR)7958

Descriptive statistics

Standard deviation4320.129918
Coefficient of variation (CV)0.7638892673
Kurtosis-1.273442666
Mean5655.44
Median Absolute Deviation (MAD)3777
Skewness0.3094810593
Sum20218198
Variance18663522.51
MonotonicityStrictly increasing
2022-10-06T17:35:07.623848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
80811
 
< 0.1%
80701
 
< 0.1%
80711
 
< 0.1%
80721
 
< 0.1%
80731
 
< 0.1%
80741
 
< 0.1%
80751
 
< 0.1%
80761
 
< 0.1%
80771
 
< 0.1%
Other values (3565)3565
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
132831
< 0.1%
132821
< 0.1%
132811
< 0.1%
132801
< 0.1%
132781
< 0.1%
132771
< 0.1%
132761
< 0.1%
132751
< 0.1%
132741
< 0.1%
132731
< 0.1%

Rank
Real number (ℝ≥0)

HIGH CORRELATION

Distinct792
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.5018182
Minimum1
Maximum1491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:07.833850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q147
median101
Q3219
95-th percentile941.3
Maximum1491
Range1490
Interquartile range (IQR)172

Descriptive statistics

Standard deviation305.0994131
Coefficient of variation (CV)1.389963034
Kurtosis4.856370024
Mean219.5018182
Median Absolute Deviation (MAD)69
Skewness2.305731727
Sum784719
Variance93085.65186
MonotonicityNot monotonic
2022-10-06T17:35:08.039615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122
 
0.6%
722
 
0.6%
3022
 
0.6%
6322
 
0.6%
422
 
0.6%
322
 
0.6%
5321
 
0.6%
1621
 
0.6%
2221
 
0.6%
621
 
0.6%
Other values (782)3359
94.0%
ValueCountFrequency (%)
122
0.6%
221
0.6%
322
0.6%
422
0.6%
520
0.6%
621
0.6%
722
0.6%
821
0.6%
921
0.6%
1020
0.6%
ValueCountFrequency (%)
14911
< 0.1%
14901
< 0.1%
14891
< 0.1%
14881
< 0.1%
14871
< 0.1%
14861
< 0.1%
14851
< 0.1%
14841
< 0.1%
14831
< 0.1%
14821
< 0.1%

Sourceid
Real number (ℝ≥0)

HIGH CORRELATION

Distinct273
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7036154253
Minimum12237
Maximum2.110105891 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:08.259616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12237
5-th percentile12962
Q124141
median25482
Q31.990019198 × 1010
95-th percentile2.110098561 × 1010
Maximum2.110105891 × 1010
Range2.110104667 × 1010
Interquartile range (IQR)1.990016783 × 1010

Descriptive statistics

Standard deviation9545631647
Coefficient of variation (CV)1.356654687
Kurtosis-1.455807508
Mean7036154253
Median Absolute Deviation (MAD)12055
Skewness0.6948689356
Sum2.515425146 × 1013
Variance9.111908354 × 1019
MonotonicityNot monotonic
2022-10-06T17:35:08.460615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1287222
 
0.6%
2444922
 
0.6%
2391722
 
0.6%
2477222
 
0.6%
2367422
 
0.6%
2477722
 
0.6%
2418222
 
0.6%
2480622
 
0.6%
2436022
 
0.6%
2373722
 
0.6%
Other values (263)3355
93.8%
ValueCountFrequency (%)
1223712
0.3%
1235818
0.5%
1240922
0.6%
1279522
0.6%
1287222
0.6%
1287422
0.6%
1288622
0.6%
1293122
0.6%
1296222
0.6%
1298122
0.6%
ValueCountFrequency (%)
2.110105891 × 10104
0.1%
2.110105882 × 10102
 
0.1%
2.110105802 × 10102
 
0.1%
2.110105575 × 10102
 
0.1%
2.110105479 × 10103
0.1%
2.110105433 × 10105
0.1%
2.110105292 × 10102
 
0.1%
2.110105274 × 10102
 
0.1%
2.110105235 × 10104
0.1%
2.110105183 × 10103
0.1%

Title
Categorical

HIGH CARDINALITY

Distinct273
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
Cognitive Psychology
 
22
Kybernetika
 
22
Journal of Intelligent and Fuzzy Systems
 
22
Knowledge-Based Systems
 
22
Applied Intelligence
 
22
Other values (268)
3465 

Length

Max length103
Median length62
Mean length35.09202797
Min length6

Characters and Unicode

Total characters125454
Distinct characters59
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st rowCognitive Psychology
2nd rowJournal of Memory and Language
3rd rowIEEE Transactions on Pattern Analysis and Machine Intelligence
4th rowAutonomous Agents and Multi-Agent Systems
5th rowIEEE Transactions on Fuzzy Systems

Common Values

ValueCountFrequency (%)
Cognitive Psychology22
 
0.6%
Kybernetika22
 
0.6%
Journal of Intelligent and Fuzzy Systems22
 
0.6%
Knowledge-Based Systems22
 
0.6%
Applied Intelligence22
 
0.6%
Minds and Machines22
 
0.6%
Engineering Applications of Artificial Intelligence22
 
0.6%
Neural Processing Letters22
 
0.6%
Journal of Intelligent and Robotic Systems: Theory and Applications22
 
0.6%
Computational Intelligence22
 
0.6%
Other values (263)3355
93.8%

Length

2022-10-06T17:35:08.683616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of1239
 
8.2%
and1216
 
8.0%
journal1131
 
7.5%
systems653
 
4.3%
intelligence617
 
4.1%
international526
 
3.5%
artificial462
 
3.1%
intelligent336
 
2.2%
on269
 
1.8%
transactions241
 
1.6%
Other values (292)8420
55.7%

Most occurring characters

ValueCountFrequency (%)
n13223
 
10.5%
11535
 
9.2%
e10326
 
8.2%
i10037
 
8.0%
t8568
 
6.8%
o8564
 
6.8%
a8416
 
6.7%
l6164
 
4.9%
r5440
 
4.3%
s5114
 
4.1%
Other values (49)38067
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter99744
79.5%
Uppercase Letter13384
 
10.7%
Space Separator11535
 
9.2%
Other Punctuation325
 
0.3%
Dash Punctuation204
 
0.2%
Open Punctuation131
 
0.1%
Close Punctuation131
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n13223
13.3%
e10326
10.4%
i10037
10.1%
t8568
8.6%
o8564
8.6%
a8416
8.4%
l6164
 
6.2%
r5440
 
5.5%
s5114
 
5.1%
c4452
 
4.5%
Other values (15)19440
19.5%
Uppercase Letter
ValueCountFrequency (%)
I2387
17.8%
A1533
11.5%
S1498
11.2%
J1205
9.0%
E1067
8.0%
C1033
7.7%
M712
 
5.3%
R691
 
5.2%
T591
 
4.4%
P541
 
4.0%
Other values (15)2126
15.9%
Other Punctuation
ValueCountFrequency (%)
:113
34.8%
,98
30.2%
/73
22.5%
'31
 
9.5%
.10
 
3.1%
Space Separator
ValueCountFrequency (%)
11535
100.0%
Dash Punctuation
ValueCountFrequency (%)
-204
100.0%
Open Punctuation
ValueCountFrequency (%)
(131
100.0%
Close Punctuation
ValueCountFrequency (%)
)131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin113128
90.2%
Common12326
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n13223
11.7%
e10326
 
9.1%
i10037
 
8.9%
t8568
 
7.6%
o8564
 
7.6%
a8416
 
7.4%
l6164
 
5.4%
r5440
 
4.8%
s5114
 
4.5%
c4452
 
3.9%
Other values (40)32824
29.0%
Common
ValueCountFrequency (%)
11535
93.6%
-204
 
1.7%
(131
 
1.1%
)131
 
1.1%
:113
 
0.9%
,98
 
0.8%
/73
 
0.6%
'31
 
0.3%
.10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n13223
 
10.5%
11535
 
9.2%
e10326
 
8.2%
i10037
 
8.0%
t8568
 
6.8%
o8564
 
6.8%
a8416
 
6.7%
l6164
 
4.9%
r5440
 
4.3%
s5114
 
4.1%
Other values (49)38067
30.3%

Type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
journal
3418 
book series
 
157

Length

Max length11
Median length7
Mean length7.175664336
Min length7

Characters and Unicode

Total characters25653
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowjournal
2nd rowjournal
3rd rowjournal
4th rowjournal
5th rowjournal

Common Values

ValueCountFrequency (%)
journal3418
95.6%
book series157
 
4.4%

Length

2022-10-06T17:35:08.890646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T17:35:09.128645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
journal3418
91.6%
book157
 
4.2%
series157
 
4.2%

Most occurring characters

ValueCountFrequency (%)
o3732
14.5%
r3575
13.9%
j3418
13.3%
u3418
13.3%
n3418
13.3%
a3418
13.3%
l3418
13.3%
s314
 
1.2%
e314
 
1.2%
b157
 
0.6%
Other values (3)471
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25496
99.4%
Space Separator157
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o3732
14.6%
r3575
14.0%
j3418
13.4%
u3418
13.4%
n3418
13.4%
a3418
13.4%
l3418
13.4%
s314
 
1.2%
e314
 
1.2%
b157
 
0.6%
Other values (2)314
 
1.2%
Space Separator
ValueCountFrequency (%)
157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25496
99.4%
Common157
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o3732
14.6%
r3575
14.0%
j3418
13.4%
u3418
13.4%
n3418
13.4%
a3418
13.4%
l3418
13.4%
s314
 
1.2%
e314
 
1.2%
b157
 
0.6%
Other values (2)314
 
1.2%
Common
ValueCountFrequency (%)
157
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o3732
14.5%
r3575
13.9%
j3418
13.3%
u3418
13.3%
n3418
13.3%
a3418
13.3%
l3418
13.3%
s314
 
1.2%
e314
 
1.2%
b157
 
0.6%
Other values (3)471
 
1.8%

Issn
Categorical

HIGH CARDINALITY

Distinct273
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
10955623, 00100285
 
22
00235954, 1805949X
 
22
18758967, 10641246
 
22
09507051
 
22
0924669X, 15737497
 
22
Other values (268)
3465 

Length

Max length18
Median length18
Mean length14.18461538
Min length8

Characters and Unicode

Total characters50710
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row10955623, 00100285
2nd row10960821, 0749596X
3rd row01628828
4th row15737454, 13872532
5th row10636706

Common Values

ValueCountFrequency (%)
10955623, 0010028522
 
0.6%
00235954, 1805949X22
 
0.6%
18758967, 1064124622
 
0.6%
0950705122
 
0.6%
0924669X, 1573749722
 
0.6%
09246495, 1572864122
 
0.6%
0952197622
 
0.6%
13704621, 1573773X22
 
0.6%
15730409, 0921029622
 
0.6%
08247935, 1467864022
 
0.6%
Other values (263)3355
93.8%

Length

2022-10-06T17:35:09.262615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1095562322
 
0.4%
1573752722
 
0.4%
0269282122
 
0.4%
1573746222
 
0.4%
0891201722
 
0.4%
1530931222
 
0.4%
0378216622
 
0.4%
0920569122
 
0.4%
1573140522
 
0.4%
0364021322
 
0.4%
Other values (420)5566
96.2%

Most occurring characters

ValueCountFrequency (%)
17076
14.0%
05523
10.9%
25075
10.0%
34346
8.6%
54292
8.5%
64185
8.3%
74002
7.9%
93839
7.6%
43715
7.3%
83621
7.1%
Other values (3)5036
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45674
90.1%
Other Punctuation2211
 
4.4%
Space Separator2211
 
4.4%
Uppercase Letter614
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17076
15.5%
05523
12.1%
25075
11.1%
34346
9.5%
54292
9.4%
64185
9.2%
74002
8.8%
93839
8.4%
43715
8.1%
83621
7.9%
Other Punctuation
ValueCountFrequency (%)
,2211
100.0%
Space Separator
ValueCountFrequency (%)
2211
100.0%
Uppercase Letter
ValueCountFrequency (%)
X614
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common50096
98.8%
Latin614
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
17076
14.1%
05523
11.0%
25075
10.1%
34346
8.7%
54292
8.6%
64185
8.4%
74002
8.0%
93839
7.7%
43715
7.4%
83621
7.2%
Other values (2)4422
8.8%
Latin
ValueCountFrequency (%)
X614
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17076
14.0%
05523
10.9%
25075
10.0%
34346
8.6%
54292
8.5%
64185
8.3%
74002
7.9%
93839
7.6%
43715
7.3%
83621
7.1%
Other values (3)5036
9.9%

SJR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1351
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6556615385
Minimum0
Maximum13.206
Zeros336
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:09.441644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.182
median0.388
Q30.7975
95-th percentile2.229
Maximum13.206
Range13.206
Interquartile range (IQR)0.6155

Descriptive statistics

Standard deviation0.8418573944
Coefficient of variation (CV)1.283981666
Kurtosis29.18078038
Mean0.6556615385
Median Absolute Deviation (MAD)0.25
Skewness3.945175071
Sum2343.99
Variance0.7087238725
MonotonicityNot monotonic
2022-10-06T17:35:09.662644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0336
 
9.4%
0.121
 
0.6%
0.10119
 
0.5%
0.18214
 
0.4%
0.11713
 
0.4%
0.12312
 
0.3%
0.13212
 
0.3%
0.10612
 
0.3%
0.20511
 
0.3%
0.24211
 
0.3%
Other values (1341)3114
87.1%
ValueCountFrequency (%)
0336
9.4%
0.121
 
0.6%
0.10119
 
0.5%
0.1029
 
0.3%
0.1038
 
0.2%
0.1048
 
0.2%
0.1057
 
0.2%
0.10612
 
0.3%
0.1074
 
0.1%
0.10811
 
0.3%
ValueCountFrequency (%)
13.2061
< 0.1%
10.4541
< 0.1%
8.2691
< 0.1%
7.5361
< 0.1%
6.8381
< 0.1%
6.7791
< 0.1%
6.5691
< 0.1%
6.2021
< 0.1%
5.9741
< 0.1%
5.6191
< 0.1%

SJR Quartile
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
Q1
796 
Q2
788 
Q3
785 
Q4
782 
-
424 

Length

Max length2
Median length2
Mean length1.881398601
Min length1

Characters and Unicode

Total characters6726
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q1796
22.3%
Q2788
22.0%
Q3785
22.0%
Q4782
21.9%
-424
11.9%

Length

2022-10-06T17:35:10.062213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T17:35:10.249168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
q1796
22.3%
q2788
22.0%
q3785
22.0%
q4782
21.9%
424
11.9%

Most occurring characters

ValueCountFrequency (%)
Q3151
46.8%
1796
 
11.8%
2788
 
11.7%
3785
 
11.7%
4782
 
11.6%
-424
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3151
46.8%
Decimal Number3151
46.8%
Dash Punctuation424
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1796
25.3%
2788
25.0%
3785
24.9%
4782
24.8%
Uppercase Letter
ValueCountFrequency (%)
Q3151
100.0%
Dash Punctuation
ValueCountFrequency (%)
-424
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3575
53.2%
Latin3151
46.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1796
22.3%
2788
22.0%
3785
22.0%
4782
21.9%
-424
11.9%
Latin
ValueCountFrequency (%)
Q3151
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Q3151
46.8%
1796
 
11.8%
2788
 
11.7%
3785
 
11.7%
4782
 
11.6%
-424
 
6.3%

H index
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.19440559
Minimum0
Maximum377
Zeros14
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:10.437164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q121
median45
Q379
95-th percentile174
Maximum377
Range377
Interquartile range (IQR)58

Descriptive statistics

Standard deviation56.91725183
Coefficient of variation (CV)0.9301054774
Kurtosis5.75250529
Mean61.19440559
Median Absolute Deviation (MAD)26
Skewness2.030310536
Sum218770
Variance3239.573556
MonotonicityNot monotonic
2022-10-06T17:35:10.645136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41124
 
3.5%
15121
 
3.4%
5788
 
2.5%
5686
 
2.4%
2185
 
2.4%
2673
 
2.0%
4673
 
2.0%
1868
 
1.9%
4566
 
1.8%
6466
 
1.8%
Other values (91)2725
76.2%
ValueCountFrequency (%)
014
 
0.4%
11
 
< 0.1%
211
 
0.3%
334
1.0%
430
0.8%
522
0.6%
631
0.9%
753
1.5%
822
0.6%
936
1.0%
ValueCountFrequency (%)
37722
0.6%
22522
0.6%
22143
1.2%
21822
0.6%
20222
0.6%
20122
0.6%
19422
0.6%
17422
0.6%
17022
0.6%
16322
0.6%

Total Docs. (3years)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct720
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.860979
Minimum0
Maximum4239
Zeros186
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:10.870166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q162
median112
Q3216
95-th percentile770.9
Maximum4239
Range4239
Interquartile range (IQR)154

Descriptive statistics

Standard deviation379.0443303
Coefficient of variation (CV)1.724018205
Kurtosis39.6493154
Mean219.860979
Median Absolute Deviation (MAD)65
Skewness5.456642423
Sum786003
Variance143674.6043
MonotonicityNot monotonic
2022-10-06T17:35:11.071164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0186
 
5.2%
7129
 
0.8%
6327
 
0.8%
7725
 
0.7%
6725
 
0.7%
9124
 
0.7%
6224
 
0.7%
5524
 
0.7%
9423
 
0.6%
10323
 
0.6%
Other values (710)3165
88.5%
ValueCountFrequency (%)
0186
5.2%
19
 
0.3%
25
 
0.1%
36
 
0.2%
44
 
0.1%
55
 
0.1%
610
 
0.3%
710
 
0.3%
84
 
0.1%
94
 
0.1%
ValueCountFrequency (%)
42391
< 0.1%
42111
< 0.1%
40841
< 0.1%
40721
< 0.1%
40271
< 0.1%
40151
< 0.1%
38091
< 0.1%
37051
< 0.1%
36331
< 0.1%
35841
< 0.1%

Total Refs.
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2406
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2950.876084
Minimum0
Maximum86050
Zeros105
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:11.280136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile178
Q1731
median1321
Q32857
95-th percentile9756.7
Maximum86050
Range86050
Interquartile range (IQR)2126

Descriptive statistics

Standard deviation5835.449234
Coefficient of variation (CV)1.977531102
Kurtosis57.93497299
Mean2950.876084
Median Absolute Deviation (MAD)746
Skewness6.519601857
Sum10549382
Variance34052467.76
MonotonicityNot monotonic
2022-10-06T17:35:11.476165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0105
 
2.9%
5758
 
0.2%
8307
 
0.2%
9207
 
0.2%
7066
 
0.2%
6906
 
0.2%
5256
 
0.2%
7046
 
0.2%
7605
 
0.1%
10705
 
0.1%
Other values (2396)3414
95.5%
ValueCountFrequency (%)
0105
2.9%
11
 
< 0.1%
51
 
< 0.1%
111
 
< 0.1%
181
 
< 0.1%
281
 
< 0.1%
321
 
< 0.1%
341
 
< 0.1%
392
 
0.1%
421
 
< 0.1%
ValueCountFrequency (%)
860501
< 0.1%
831701
< 0.1%
739801
< 0.1%
702661
< 0.1%
603921
< 0.1%
584861
< 0.1%
572121
< 0.1%
549121
< 0.1%
547551
< 0.1%
532261
< 0.1%

Total Cites (3years)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1130
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean626.7267133
Minimum0
Maximum26359
Zeros202
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:11.673165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q151
median152
Q3428
95-th percentile2452.7
Maximum26359
Range26359
Interquartile range (IQR)377

Descriptive statistics

Standard deviation1828.225794
Coefficient of variation (CV)2.917102072
Kurtosis62.54154901
Mean626.7267133
Median Absolute Deviation (MAD)128
Skewness7.055249153
Sum2240548
Variance3342409.553
MonotonicityNot monotonic
2022-10-06T17:35:11.882792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0202
 
5.7%
1035
 
1.0%
1323
 
0.6%
222
 
0.6%
922
 
0.6%
2320
 
0.6%
820
 
0.6%
520
 
0.6%
719
 
0.5%
619
 
0.5%
Other values (1120)3173
88.8%
ValueCountFrequency (%)
0202
5.7%
116
 
0.4%
222
 
0.6%
311
 
0.3%
413
 
0.4%
520
 
0.6%
619
 
0.5%
719
 
0.5%
820
 
0.6%
922
 
0.6%
ValueCountFrequency (%)
263591
< 0.1%
240231
< 0.1%
238601
< 0.1%
208511
< 0.1%
205361
< 0.1%
204201
< 0.1%
181911
< 0.1%
165891
< 0.1%
164051
< 0.1%
161471
< 0.1%

Citable Docs. (3years)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct700
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.285035
Minimum0
Maximum4154
Zeros190
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:12.090236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156
median102
Q3201
95-th percentile765
Maximum4154
Range4154
Interquartile range (IQR)145

Descriptive statistics

Standard deviation361.2253979
Coefficient of variation (CV)1.742650635
Kurtosis41.03255733
Mean207.285035
Median Absolute Deviation (MAD)61
Skewness5.474647023
Sum741044
Variance130483.7881
MonotonicityNot monotonic
2022-10-06T17:35:12.293760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0190
 
5.3%
7830
 
0.8%
6528
 
0.8%
8228
 
0.8%
6326
 
0.7%
6626
 
0.7%
7126
 
0.7%
6425
 
0.7%
5625
 
0.7%
8925
 
0.7%
Other values (690)3146
88.0%
ValueCountFrequency (%)
0190
5.3%
114
 
0.4%
27
 
0.2%
36
 
0.2%
48
 
0.2%
57
 
0.2%
68
 
0.2%
717
 
0.5%
81
 
< 0.1%
96
 
0.2%
ValueCountFrequency (%)
41541
< 0.1%
41421
< 0.1%
40741
< 0.1%
40691
< 0.1%
39691
< 0.1%
39421
< 0.1%
37971
< 0.1%
35721
< 0.1%
35331
< 0.1%
32861
< 0.1%

Cites / Doc. (2years)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct708
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.077756643
Minimum0
Maximum106
Zeros227
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:12.502007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.53
median1.24
Q32.665
95-th percentile6.436
Maximum106
Range106
Interquartile range (IQR)2.135

Descriptive statistics

Standard deviation3.36003226
Coefficient of variation (CV)1.617144275
Kurtosis298.805745
Mean2.077756643
Median Absolute Deviation (MAD)0.87
Skewness12.3582909
Sum7427.98
Variance11.28981679
MonotonicityNot monotonic
2022-10-06T17:35:12.718929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0227
 
6.3%
125
 
0.7%
0.5224
 
0.7%
0.4523
 
0.6%
0.823
 
0.6%
0.7822
 
0.6%
0.6822
 
0.6%
0.3321
 
0.6%
0.3820
 
0.6%
0.1420
 
0.6%
Other values (698)3148
88.1%
ValueCountFrequency (%)
0227
6.3%
0.014
 
0.1%
0.028
 
0.2%
0.039
 
0.3%
0.043
 
0.1%
0.057
 
0.2%
0.066
 
0.2%
0.0715
 
0.4%
0.0810
 
0.3%
0.0915
 
0.4%
ValueCountFrequency (%)
1061
< 0.1%
541
< 0.1%
531
< 0.1%
39.561
< 0.1%
38.781
< 0.1%
27.061
< 0.1%
25.291
< 0.1%
25.211
< 0.1%
22.51
< 0.1%
22.431
< 0.1%

Ref. / Doc.
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2427
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.38836084
Minimum0
Maximum390
Zeros105
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:12.933155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.535
Q123.28
median31.61
Q341.505
95-th percentile60.683
Maximum390
Range390
Interquartile range (IQR)18.225

Descriptive statistics

Standard deviation22.2820181
Coefficient of variation (CV)0.6479523174
Kurtosis50.48406977
Mean34.38836084
Median Absolute Deviation (MAD)8.96
Skewness5.124727005
Sum122938.39
Variance496.4883304
MonotonicityNot monotonic
2022-10-06T17:35:13.157153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0105
 
2.9%
306
 
0.2%
396
 
0.2%
336
 
0.2%
326
 
0.2%
35.266
 
0.2%
28.255
 
0.1%
285
 
0.1%
40.295
 
0.1%
27.885
 
0.1%
Other values (2417)3420
95.7%
ValueCountFrequency (%)
0105
2.9%
11
 
< 0.1%
1.981
 
< 0.1%
2.681
 
< 0.1%
3.211
 
< 0.1%
4.51
 
< 0.1%
4.611
 
< 0.1%
4.841
 
< 0.1%
4.891
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
3901
< 0.1%
3141
< 0.1%
2821
< 0.1%
2761
< 0.1%
243.671
< 0.1%
241.711
< 0.1%
2381
< 0.1%
235.331
< 0.1%
225.81
< 0.1%
213.671
< 0.1%

Country
Categorical

HIGH CORRELATION

Distinct34
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
United States
876 
Netherlands
849 
United Kingdom
690 
Switzerland
160 
Germany
157 
Other values (29)
843 

Length

Max length18
Median length14
Mean length11.11916084
Min length4

Characters and Unicode

Total characters39751
Distinct characters39
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowNetherlands
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States876
24.5%
Netherlands849
23.7%
United Kingdom690
19.3%
Switzerland160
 
4.5%
Germany157
 
4.4%
Singapore155
 
4.3%
China96
 
2.7%
Japan75
 
2.1%
Czech Republic61
 
1.7%
Poland47
 
1.3%
Other values (24)409
11.4%

Length

2022-10-06T17:35:13.376175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united1566
29.8%
states876
16.7%
netherlands849
16.2%
kingdom690
13.1%
switzerland160
 
3.0%
germany157
 
3.0%
singapore155
 
2.9%
china96
 
1.8%
japan75
 
1.4%
republic61
 
1.2%
Other values (28)571
 
10.9%

Most occurring characters

ValueCountFrequency (%)
e4958
12.5%
t4458
11.2%
n4164
10.5%
d3454
 
8.7%
a3019
 
7.6%
i3012
 
7.6%
s1868
 
4.7%
1681
 
4.2%
U1578
 
4.0%
r1507
 
3.8%
Other values (29)10052
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32814
82.5%
Uppercase Letter5256
 
13.2%
Space Separator1681
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4958
15.1%
t4458
13.6%
n4164
12.7%
d3454
10.5%
a3019
9.2%
i3012
9.2%
s1868
 
5.7%
r1507
 
4.6%
l1224
 
3.7%
h1034
 
3.2%
Other values (12)4116
12.5%
Uppercase Letter
ValueCountFrequency (%)
U1578
30.0%
S1250
23.8%
N849
16.2%
K708
13.5%
C183
 
3.5%
G172
 
3.3%
I110
 
2.1%
R97
 
1.8%
F84
 
1.6%
J75
 
1.4%
Other values (6)150
 
2.9%
Space Separator
ValueCountFrequency (%)
1681
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin38070
95.8%
Common1681
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4958
13.0%
t4458
11.7%
n4164
10.9%
d3454
 
9.1%
a3019
 
7.9%
i3012
 
7.9%
s1868
 
4.9%
U1578
 
4.1%
r1507
 
4.0%
S1250
 
3.3%
Other values (28)8802
23.1%
Common
ValueCountFrequency (%)
1681
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII39751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4958
12.5%
t4458
11.2%
n4164
10.5%
d3454
 
8.7%
a3019
 
7.6%
i3012
 
7.6%
s1868
 
4.7%
1681
 
4.2%
U1578
 
4.0%
r1507
 
3.8%
Other values (29)10052
25.3%

Region
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
Western Europe
1986 
Northern America
902 
Asiatic Region
432 
Eastern Europe
 
171
Middle East
 
44
Other values (3)
 
40

Length

Max length18
Median length14
Mean length14.47132867
Min length11

Characters and Unicode

Total characters51735
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorthern America
2nd rowNorthern America
3rd rowNorthern America
4th rowWestern Europe
5th rowNorthern America

Common Values

ValueCountFrequency (%)
Western Europe1986
55.6%
Northern America902
25.2%
Asiatic Region432
 
12.1%
Eastern Europe171
 
4.8%
Middle East44
 
1.2%
Pacific Region23
 
0.6%
Latin America11
 
0.3%
Africa/Middle East6
 
0.2%

Length

2022-10-06T17:35:13.559203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-06T17:35:13.756206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
europe2157
30.2%
western1986
27.8%
america913
12.8%
northern902
12.6%
region455
 
6.4%
asiatic432
 
6.0%
eastern171
 
2.4%
east50
 
0.7%
middle44
 
0.6%
pacific23
 
0.3%
Other values (2)17
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e8620
16.7%
r7037
13.6%
3575
 
6.9%
t3552
 
6.9%
n3525
 
6.8%
o3514
 
6.8%
s2639
 
5.1%
E2378
 
4.6%
i2345
 
4.5%
u2157
 
4.2%
Other values (17)12393
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40998
79.2%
Uppercase Letter7156
 
13.8%
Space Separator3575
 
6.9%
Other Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8620
21.0%
r7037
17.2%
t3552
8.7%
n3525
8.6%
o3514
8.6%
s2639
 
6.4%
i2345
 
5.7%
u2157
 
5.3%
p2157
 
5.3%
a1606
 
3.9%
Other values (7)3846
9.4%
Uppercase Letter
ValueCountFrequency (%)
E2378
33.2%
W1986
27.8%
A1351
18.9%
N902
 
12.6%
R455
 
6.4%
M50
 
0.7%
P23
 
0.3%
L11
 
0.2%
Space Separator
ValueCountFrequency (%)
3575
100.0%
Other Punctuation
ValueCountFrequency (%)
/6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin48154
93.1%
Common3581
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8620
17.9%
r7037
14.6%
t3552
 
7.4%
n3525
 
7.3%
o3514
 
7.3%
s2639
 
5.5%
E2378
 
4.9%
i2345
 
4.9%
u2157
 
4.5%
p2157
 
4.5%
Other values (15)10230
21.2%
Common
ValueCountFrequency (%)
3575
99.8%
/6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII51735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8620
16.7%
r7037
13.6%
3575
 
6.9%
t3552
 
6.9%
n3525
 
6.8%
o3514
 
6.8%
s2639
 
5.1%
E2378
 
4.6%
i2345
 
4.5%
u2157
 
4.2%
Other values (17)12393
24.0%

Publisher
Categorical

HIGH CARDINALITY
MISSING

Distinct134
Distinct (%)3.8%
Missing47
Missing (%)1.3%
Memory size28.1 KiB
Springer Netherlands
396 
Elsevier
245 
IOS Press BV
 
172
Elsevier Ltd.
 
155
World Scientific Publishing Co. Pte Ltd
 
136
Other values (129)
2424 

Length

Max length90
Median length61
Mean length24.48185941
Min length7

Characters and Unicode

Total characters86372
Distinct characters60
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowAcademic Press Inc.
2nd rowAcademic Press Inc.
3rd rowIEEE Computer Society
4th rowSpringer Netherlands
5th rowInstitute of Electrical and Electronics Engineers Inc.

Common Values

ValueCountFrequency (%)
Springer Netherlands396
 
11.1%
Elsevier245
 
6.9%
IOS Press BV172
 
4.8%
Elsevier Ltd.155
 
4.3%
World Scientific Publishing Co. Pte Ltd136
 
3.8%
Institute of Electrical and Electronics Engineers Inc.124
 
3.5%
Springer Verlag109
 
3.0%
Taylor and Francis Ltd.96
 
2.7%
Cambridge University Press87
 
2.4%
Springer London83
 
2.3%
Other values (124)1925
53.8%

Length

2022-10-06T17:35:13.979204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
springer826
 
7.1%
ltd542
 
4.6%
press522
 
4.5%
elsevier485
 
4.1%
and454
 
3.9%
netherlands396
 
3.4%
of387
 
3.3%
inc375
 
3.2%
publishing357
 
3.0%
university249
 
2.1%
Other values (247)7119
60.8%

Most occurring characters

ValueCountFrequency (%)
8184
 
9.5%
e8120
 
9.4%
i6928
 
8.0%
n6393
 
7.4%
r6010
 
7.0%
s4873
 
5.6%
t4157
 
4.8%
a3846
 
4.5%
l3574
 
4.1%
c3480
 
4.0%
Other values (50)30807
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64494
74.7%
Uppercase Letter12360
 
14.3%
Space Separator8184
 
9.5%
Other Punctuation1046
 
1.2%
Dash Punctuation97
 
0.1%
Open Punctuation74
 
0.1%
Close Punctuation74
 
0.1%
Math Symbol43
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8120
12.6%
i6928
10.7%
n6393
9.9%
r6010
9.3%
s4873
 
7.6%
t4157
 
6.4%
a3846
 
6.0%
l3574
 
5.5%
c3480
 
5.4%
o3208
 
5.0%
Other values (15)13905
21.6%
Uppercase Letter
ValueCountFrequency (%)
S1991
16.1%
I1357
11.0%
P1350
10.9%
E1345
10.9%
A956
 
7.7%
L704
 
5.7%
C666
 
5.4%
N558
 
4.5%
M492
 
4.0%
G392
 
3.2%
Other values (14)2549
20.6%
Other Punctuation
ValueCountFrequency (%)
.887
84.8%
,106
 
10.1%
/22
 
2.1%
&11
 
1.1%
;11
 
1.1%
'9
 
0.9%
Space Separator
ValueCountFrequency (%)
8184
100.0%
Dash Punctuation
ValueCountFrequency (%)
-97
100.0%
Open Punctuation
ValueCountFrequency (%)
(74
100.0%
Close Punctuation
ValueCountFrequency (%)
)74
100.0%
Math Symbol
ValueCountFrequency (%)
+43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin76854
89.0%
Common9518
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8120
 
10.6%
i6928
 
9.0%
n6393
 
8.3%
r6010
 
7.8%
s4873
 
6.3%
t4157
 
5.4%
a3846
 
5.0%
l3574
 
4.7%
c3480
 
4.5%
o3208
 
4.2%
Other values (39)26265
34.2%
Common
ValueCountFrequency (%)
8184
86.0%
.887
 
9.3%
,106
 
1.1%
-97
 
1.0%
(74
 
0.8%
)74
 
0.8%
+43
 
0.5%
/22
 
0.2%
&11
 
0.1%
;11
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII86372
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8184
 
9.5%
e8120
 
9.4%
i6928
 
8.0%
n6393
 
7.4%
r6010
 
7.0%
s4873
 
5.6%
t4157
 
4.8%
a3846
 
4.5%
l3574
 
4.1%
c3480
 
4.0%
Other values (50)30807
35.7%

Coverage
Categorical

HIGH CARDINALITY

Distinct104
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
1995-2021
 
132
2004-2021
 
126
1987-2021
 
110
1993-2021
 
110
1989-2021
 
110
Other values (99)
2987 

Length

Max length38
Median length9
Mean length10.37622378
Min length4

Characters and Unicode

Total characters37095
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row1970-2021
2nd row1985-2021
3rd row1978-2021
4th row1998-2021
5th row1993-2021

Common Values

ValueCountFrequency (%)
1995-2021132
 
3.7%
2004-2021126
 
3.5%
1987-2021110
 
3.1%
1993-2021110
 
3.1%
1989-2021110
 
3.1%
2001-2021106
 
3.0%
1996-202188
 
2.5%
2005-202185
 
2.4%
2008-202184
 
2.3%
2010-202184
 
2.3%
Other values (94)2540
71.0%

Length

2022-10-06T17:35:14.174275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2004-2021167
 
4.0%
1996-2021154
 
3.7%
1995-2021154
 
3.7%
1987-2021154
 
3.7%
2001-2021128
 
3.1%
1993-2021110
 
2.6%
1989-2021110
 
2.6%
2012-2021100
 
2.4%
1998-202188
 
2.1%
1997-202188
 
2.1%
Other values (112)2907
69.9%

Most occurring characters

ValueCountFrequency (%)
29667
26.1%
07255
19.6%
16817
18.4%
93988
10.8%
-3857
 
10.4%
81439
 
3.9%
7875
 
2.4%
6612
 
1.6%
5588
 
1.6%
,585
 
1.6%
Other values (3)1412
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32068
86.4%
Dash Punctuation3857
 
10.4%
Other Punctuation585
 
1.6%
Space Separator585
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
29667
30.1%
07255
22.6%
16817
21.3%
93988
12.4%
81439
 
4.5%
7875
 
2.7%
6612
 
1.9%
5588
 
1.8%
4546
 
1.7%
3281
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
-3857
100.0%
Other Punctuation
ValueCountFrequency (%)
,585
100.0%
Space Separator
ValueCountFrequency (%)
585
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common37095
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
29667
26.1%
07255
19.6%
16817
18.4%
93988
10.8%
-3857
 
10.4%
81439
 
3.9%
7875
 
2.4%
6612
 
1.6%
5588
 
1.6%
,585
 
1.6%
Other values (3)1412
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII37095
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29667
26.1%
07255
19.6%
16817
18.4%
93988
10.8%
-3857
 
10.4%
81439
 
3.9%
7875
 
2.4%
6612
 
1.6%
5588
 
1.6%
,585
 
1.6%
Other values (3)1412
 
3.8%

Categories
Categorical

HIGH CARDINALITY

Distinct1033
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
Artificial Intelligence (Q4); Software (Q4)
 
88
Artificial Intelligence (Q2)
 
85
Artificial Intelligence (Q3)
 
72
Artificial Intelligence (Q1)
 
66
Artificial Intelligence (Q1); Linguistics and Language (Q1)
 
34
Other values (1028)
3230 

Length

Max length314
Median length231
Mean length107.9762238
Min length10

Characters and Unicode

Total characters386015
Distinct characters54
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)14.2%

Sample

1st rowArtificial Intelligence (Q1); Developmental and Educational Psychology (Q1); Experimental and Cognitive Psychology (Q1); Linguistics and Language (Q1); Neuropsychology and Physiological Psychology (Q1)
2nd rowArtificial Intelligence (Q1); Experimental and Cognitive Psychology (Q1); Linguistics and Language (Q1); Neuropsychology and Physiological Psychology (Q1)
3rd rowApplied Mathematics (Q1); Artificial Intelligence (Q1); Computational Theory and Mathematics (Q1); Computer Vision and Pattern Recognition (Q1); Software (Q1)
4th rowArtificial Intelligence (Q1)
5th rowApplied Mathematics (Q1); Artificial Intelligence (Q1); Computational Theory and Mathematics (Q1); Control and Systems Engineering (Q1)

Common Values

ValueCountFrequency (%)
Artificial Intelligence (Q4); Software (Q4)88
 
2.5%
Artificial Intelligence (Q2)85
 
2.4%
Artificial Intelligence (Q3)72
 
2.0%
Artificial Intelligence (Q1)66
 
1.8%
Artificial Intelligence (Q1); Linguistics and Language (Q1)34
 
1.0%
Artificial Intelligence (Q4)33
 
0.9%
Artificial Intelligence (Q1); Software (Q1)26
 
0.7%
Artificial Intelligence (Q1); Computer Vision and Pattern Recognition (Q1); Software (Q1)26
 
0.7%
Artificial Intelligence (Q2); Software (Q2)25
 
0.7%
Artificial Intelligence (Q4); Computer Networks and Communications (Q4); Software (Q4)25
 
0.7%
Other values (1023)3095
86.6%

Length

2022-10-06T17:35:14.378953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and3935
 
9.0%
artificial3480
 
8.0%
intelligence3480
 
8.0%
q13237
 
7.4%
q32744
 
6.3%
q42662
 
6.1%
q22647
 
6.1%
computer2029
 
4.6%
software1538
 
3.5%
engineering1370
 
3.1%
Other values (141)16545
37.9%

Most occurring characters

ValueCountFrequency (%)
40092
 
10.4%
i31724
 
8.2%
e31605
 
8.2%
n28019
 
7.3%
t23587
 
6.1%
a19535
 
5.1%
c17685
 
4.6%
l17178
 
4.5%
o16571
 
4.3%
r15797
 
4.1%
Other values (44)144222
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter261716
67.8%
Space Separator40092
 
10.4%
Uppercase Letter39433
 
10.2%
Close Punctuation11916
 
3.1%
Open Punctuation11916
 
3.1%
Decimal Number11290
 
2.9%
Other Punctuation9269
 
2.4%
Dash Punctuation383
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i31724
12.1%
e31605
12.1%
n28019
10.7%
t23587
9.0%
a19535
 
7.5%
c17685
 
6.8%
l17178
 
6.6%
o16571
 
6.3%
r15797
 
6.0%
g9685
 
3.7%
Other values (14)50330
19.2%
Uppercase Letter
ValueCountFrequency (%)
Q11290
28.6%
A4757
12.1%
I4618
11.7%
C4518
11.5%
S4476
 
11.4%
E2365
 
6.0%
M1488
 
3.8%
P1197
 
3.0%
N874
 
2.2%
T755
 
1.9%
Other values (10)3095
 
7.8%
Decimal Number
ValueCountFrequency (%)
13237
28.7%
32744
24.3%
42662
23.6%
22647
23.4%
Other Punctuation
ValueCountFrequency (%)
;9103
98.2%
,166
 
1.8%
Space Separator
ValueCountFrequency (%)
40092
100.0%
Close Punctuation
ValueCountFrequency (%)
)11916
100.0%
Open Punctuation
ValueCountFrequency (%)
(11916
100.0%
Dash Punctuation
ValueCountFrequency (%)
-383
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin301149
78.0%
Common84866
 
22.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i31724
 
10.5%
e31605
 
10.5%
n28019
 
9.3%
t23587
 
7.8%
a19535
 
6.5%
c17685
 
5.9%
l17178
 
5.7%
o16571
 
5.5%
r15797
 
5.2%
Q11290
 
3.7%
Other values (34)88158
29.3%
Common
ValueCountFrequency (%)
40092
47.2%
)11916
 
14.0%
(11916
 
14.0%
;9103
 
10.7%
13237
 
3.8%
32744
 
3.2%
42662
 
3.1%
22647
 
3.1%
-383
 
0.5%
,166
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII386015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40092
 
10.4%
i31724
 
8.2%
e31605
 
8.2%
n28019
 
7.3%
t23587
 
6.1%
a19535
 
5.1%
c17685
 
4.6%
l17178
 
4.5%
o16571
 
4.3%
r15797
 
4.1%
Other values (44)144222
37.4%

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.398322
Minimum2000
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:14.572335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12008
median2013
Q32018
95-th percentile2021
Maximum2021
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.163478684
Coefficient of variation (CV)0.003062752844
Kurtosis-1.018533773
Mean2012.398322
Median Absolute Deviation (MAD)5
Skewness-0.3669761042
Sum7194324
Variance37.98846948
MonotonicityIncreasing
2022-10-06T17:35:14.765301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2020249
 
7.0%
2021247
 
6.9%
2019237
 
6.6%
2018226
 
6.3%
2017217
 
6.1%
2016202
 
5.7%
2015189
 
5.3%
2014180
 
5.0%
2013171
 
4.8%
2012167
 
4.7%
Other values (12)1490
41.7%
ValueCountFrequency (%)
200095
2.7%
200199
2.8%
2002100
2.8%
2003103
2.9%
2004113
3.2%
2005121
3.4%
2006129
3.6%
2007132
3.7%
2008138
3.9%
2009145
4.1%
ValueCountFrequency (%)
2021247
6.9%
2020249
7.0%
2019237
6.6%
2018226
6.3%
2017217
6.1%
2016202
5.7%
2015189
5.3%
2014180
5.0%
2013171
4.8%
2012167
4.7%

Total Docs. per Year
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct431
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.31272727
Minimum0
Maximum1776
Zeros84
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2022-10-06T17:35:14.945342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q125
median43
Q384.5
95-th percentile315.3
Maximum1776
Range1776
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation156.7882711
Coefficient of variation (CV)1.775375712
Kurtosis38.79432467
Mean88.31272727
Median Absolute Deviation (MAD)23
Skewness5.486954542
Sum315718
Variance24582.56194
MonotonicityNot monotonic
2022-10-06T17:35:15.154483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
084
 
2.3%
2469
 
1.9%
2265
 
1.8%
2664
 
1.8%
2061
 
1.7%
2758
 
1.6%
3158
 
1.6%
2557
 
1.6%
2857
 
1.6%
3055
 
1.5%
Other values (421)2947
82.4%
ValueCountFrequency (%)
084
2.3%
118
 
0.5%
211
 
0.3%
321
 
0.6%
414
 
0.4%
513
 
0.4%
619
 
0.5%
717
 
0.5%
818
 
0.5%
914
 
0.4%
ValueCountFrequency (%)
17761
< 0.1%
17291
< 0.1%
17231
< 0.1%
17101
< 0.1%
15471
< 0.1%
14931
< 0.1%
14741
< 0.1%
13951
< 0.1%
13841
< 0.1%
13701
< 0.1%

Interactions

2022-10-06T17:35:03.712652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:36.484405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:38.892652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:41.226574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:43.529786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:45.758437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:48.067564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:50.432409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:52.591405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:54.775800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:56.887563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:59.111586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:35:01.586358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:35:03.888620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:36.700135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:39.070653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-06T17:34:41.394573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-10-06T17:35:03.542648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-06T17:35:15.358451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-06T17:35:15.636481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-06T17:35:15.913452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-06T17:35:16.166482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-06T17:35:16.362531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-06T17:35:06.134888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-06T17:35:06.776818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-06T17:35:07.017848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexRankSourceidTitleTypeIssnSJRSJR QuartileH indexTotal Docs. (3years)Total Refs.Total Cites (3years)Citable Docs. (3years)Cites / Doc. (2years)Ref. / Doc.CountryRegionPublisherCoverageCategoriesYearTotal Docs. per Year
00112872Cognitive Psychologyjournal10955623, 001002853.109Q1123631294276633.6176.12United StatesNorthern AmericaAcademic Press Inc.1970-2021Artificial Intelligence (Q1); Developmental and Educational Psychology (Q1); Experimental and Cognitive Psychology (Q1); Linguistics and Language (Q1); Neuropsychology and Physiological Psychology (Q1)200017.0
11222478Journal of Memory and Languagejournal10960821, 0749596X2.933Q115616028304751602.7247.17United StatesNorthern AmericaAcademic Press Inc.1985-2021Artificial Intelligence (Q1); Experimental and Cognitive Psychology (Q1); Linguistics and Language (Q1); Neuropsychology and Physiological Psychology (Q1)200060.0
22324254IEEE Transactions on Pattern Analysis and Machine Intelligencejournal016288281.698Q1377427417617524273.3632.63United StatesNorthern AmericaIEEE Computer Society1978-2021Applied Mathematics (Q1); Artificial Intelligence (Q1); Computational Theory and Mathematics (Q1); Computer Vision and Pattern Recognition (Q1); Software (Q1)2000128.0
33424157Autonomous Agents and Multi-Agent Systemsjournal15737454, 138725321.679Q1722560194253.7642.93NetherlandsWestern EuropeSpringer Netherlands1998-2021Artificial Intelligence (Q1)200014.0
44524242IEEE Transactions on Fuzzy Systemsjournal106367061.582Q120218017483881751.9624.28United StatesNorthern AmericaInstitute of Electrical and Electronics Engineers Inc.1993-2021Applied Mathematics (Q1); Artificial Intelligence (Q1); Computational Theory and Mathematics (Q1); Control and Systems Engineering (Q1)200072.0
55623127Journal of the ACMjournal00045411, 1557735X1.287Q1131951222240942.5634.91United StatesNorthern AmericaAssociation for Computing Machinery (ACM)1954-2021Artificial Intelligence (Q1); Control and Systems Engineering (Q1); Hardware and Architecture (Q1); Information Systems (Q1); Software (Q1)200035.0
66718050International Journal of Robotics Researchjournal02783649, 174131761.192Q117026020613312581.2728.63United StatesNorthern AmericaSAGE Publications Inc.1982-2021Applied Mathematics (Q1); Artificial Intelligence (Q1); Electrical and Electronic Engineering (Q1); Mechanical Engineering (Q1); Modeling and Simulation (Q1); Software (Q1)200072.0
77812874Cognitive Sciencejournal03640213, 155167091.090Q1120491458104491.6972.90United StatesNorthern AmericaWiley-Blackwell1977-2021Artificial Intelligence (Q1); Cognitive Neuroscience (Q2); Experimental and Cognitive Psychology (Q2)200020.0
88972242International Journal of Computer Visionjournal09205691, 157314051.050Q120117823065421782.7236.03NetherlandsWestern EuropeSpringer Netherlands1987-2021Artificial Intelligence (Q1); Computer Vision and Pattern Recognition (Q1); Software (Q1)200064.0
991023675Artificial Intelligencejournal000437021.018Q115525833916152571.9445.82NetherlandsWestern EuropeElsevier1970-2022Artificial Intelligence (Q1); Linguistics and Language (Q1)200074.0

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df_indexRankSourceidTitleTypeIssnSJRSJR QuartileH indexTotal Docs. (3years)Total Refs.Total Cites (3years)Citable Docs. (3years)Cites / Doc. (2years)Ref. / Doc.CountryRegionPublisherCoverageCategoriesYearTotal Docs. per Year
35651327324321100327713International Journal of Automation and Smart Technologyjournal222397660.121Q41152021520.540.00TaiwanAsiatic RegionChinese Institute of Automation Engineers (CIAE)2011-2020Artificial Intelligence (Q4); Control and Systems Engineering (Q4); Electrical and Electronic Engineering (Q4); Hardware and Architecture (Q4); Human-Computer Interaction (Q4); Signal Processing (Q4)20210.0
35661327424421101037310Register: Jurnal Ilmiah Teknologi Sistem Informasijournal25023357, 250304770.120Q444244415420.3631.71IndonesiaAsiatic RegionUniversitas Pesantren Tinggi Darul Ulum2015-2022Artificial Intelligence (Q4); Computer Science (miscellaneous) (Q4); Decision Sciences (miscellaneous) (Q4); Information Systems (Q4); Information Systems and Management (Q4)202114.0
356713275245145264Transactions of the Japanese Society for Artificial Intelligencejournal134607140.117Q4171061059201060.1821.61JapanAsiatic RegionJapanese Society for Artificial Intelligence2001-2021Artificial Intelligence (Q4); Software (Q4)202149.0
35681327624621100380994Lecture Notes in Computational Vision and Biomechanicsbook series22129391, 221294130.114Q41664005371260.590.00SwitzerlandWestern EuropeSpringer International Publishing AG2012-2020Artificial Intelligence (Q4); Biomedical Engineering (Q4); Computer Science Applications (Q4); Computer Vision and Pattern Recognition (Q4); Mechanical Engineering (Q4); Signal Processing (Q4)20210.0
35691327724721101052735International Journal of Asia Digital Art and Designjournal21897441, 173880740.110Q40398030.0012.25JapanAsiatic RegionAsia Digital Art and Design Association2020-2021Visual Arts and Performing Arts (Q3); Artificial Intelligence (Q4); Computer Graphics and Computer-Aided Design (Q4); Computer Networks and Communications (Q4); Software (Q4)20218.0
3570132782485800207596Linguisticajournal2079312X, 113202140.102Q44631894570.0727.00UruguayLatin AmericaAsociacion de Linguistica y Filologia de la America Latina2012-2021Artificial Intelligence (Q4); Linguistics and Language (Q4)20217.0
35711328025021101042133Hellenic Journal of Radiologyjournal26541629, 252905680.000-00764000.0024.65GreeceWestern EuropeZita Medical Managent2021Artificial Intelligence; Computer Science Applications; Radiological and Ultrasound Technology; Radiology, Nuclear Medicine and Imaging202131.0
35721328125121100902642International Journal of Computational Intelligence in Control (discontinued)journal097485710.000-44164033400.0380.00IndiaAsiatic RegionMuk Publications and Distributions2018-2021Artificial Intelligence; Biotechnology; Computational Mechanics; Computer Science Applications; Information Systems and Management20218.0
35731328225221101049065International Journal of Interactive Multimedia and Artificial Intelligencejournal198916600.000-303836000.0046.22SpainWestern EuropeUniversidad Internacional de la Rioja2021Artificial Intelligence; Computer Networks and Communications; Computer Science Applications; Computer Vision and Pattern Recognition; Signal Processing; Statistics and Probability202183.0
35741328325321101044836Journal of Computer Science and Technology(Argentina)journal16666046, 166660380.000-10478000.0025.16ArgentinaLatin AmericaFacultad de Informatica, Universidad Nacional de La Plata2021Artificial Intelligence; Computer Science Applications; Computer Science (miscellaneous); Computer Vision and Pattern Recognition; Hardware and Architecture; Software202119.0